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source("tianfengRwrappers.R")

library(future)
plan("multiprocess",workers = 8)

CA_dataset2 <- readRDS("CA_dataset2.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
human_coronary <- readRDS("human_coronary.rds")
Idents(human_coronary) <- human_coronary$samples
human_coronary <- RenameIdents(human_coronary,'1' = 'sample1','2' = 'sample2','3' = 'sample3','4' = 'sample4')
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$Classification1
ds2 <- readRDS("ds2.rds")

#sample info

ggsave("dataset2_sampleinfo.svg",plot = umapplot(CA_dataset2, split.by = "sample"), 
       device = svg, width = 25, height = 5)
ggsave("dataset1_sampleinfo.svg",plot = umapplot(CA_dataset1, split.by = "orig.ident"),
       device = svg, width = 15, height = 5)
ggsave("dataset0_sampleinfo.svg",plot = umapplot(human_coronary, split.by = "samples"),
       device = svg, width = 20, height = 5)

附图：所有marker基因表达热图 show 表达量最高的top5

logfc.threshold = 0.5, min.diff.pct = 0.3 pct.1>0.7

dataset2

svg(paste0("CA_dataset2_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset2_markers$gene, CA_dataset2, genes_to_show$gene,"CA_dataset2_supp")
dev.off()

null device 
          1 

dataset1

svg(paste0("CA_dataset1_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset1_markers$gene, CA_dataset1, genes_to_show$gene,"CA_dataset1_supp")
dev.off()

null device 
          1 

dataset0

human_coronary_markers <- FindAllMarkers(human_coronary, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)

Calculating cluster FB

Calculating cluster Macrophage

Calculating cluster EC

Calculating cluster SMC

Calculating cluster T cell

Calculating cluster B cell

Calculating cluster Neuron

Calculating cluster Plasma

human_coronary_markers <- human_coronary_markers[human_coronary_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- human_coronary_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("human_coronary_supp","_markers.svg"), height = 10, width = 15)
dhm2(human_coronary_markers$gene, human_coronary, genes_to_show$gene,"human_coronary_supp")
dev.off()

RStudioGD 
        2 

样本细胞比例

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[1:4]))+theme(

错误: 意外的')' in:
"  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[1:4]))"

XGBoost feature

pretrain

fea <- read.csv("./datatable/AC_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACpretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PA_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PApretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

model

fea <- read.csv("./datatable/ACtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PAtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PAmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

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